Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/15721#discussion_r93765319
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala
---
@@ -157,50 +162,26 @@ class NaiveBayesSuite extends SparkFunSuite with
MLlibTestSparkContext with Defa
validateProbabilities(featureAndProbabilities, model, "multinomial")
}
- test("Naive Bayes Multinomial with weighted samples") {
- val nPoints = 1000
- val piArray = Array(0.5, 0.1, 0.4).map(math.log)
- val thetaArray = Array(
- Array(0.70, 0.10, 0.10, 0.10), // label 0
- Array(0.10, 0.70, 0.10, 0.10), // label 1
- Array(0.10, 0.10, 0.70, 0.10) // label 2
- ).map(_.map(math.log))
-
- val testData = generateNaiveBayesInput(piArray, thetaArray, nPoints,
42, "multinomial").toDF()
- val (overSampledData, weightedData) =
- MLTestingUtils.genEquivalentOversampledAndWeightedInstances(testData,
- "label", "features", 42L)
- val nb = new NaiveBayes().setModelType("multinomial")
- val unweightedModel = nb.fit(weightedData)
- val overSampledModel = nb.fit(overSampledData)
- val weightedModel = nb.setWeightCol("weight").fit(weightedData)
- assert(weightedModel.theta ~== overSampledModel.theta relTol 0.001)
- assert(weightedModel.pi ~== overSampledModel.pi relTol 0.001)
- assert(unweightedModel.theta !~= overSampledModel.theta relTol 0.001)
- assert(unweightedModel.pi !~= overSampledModel.pi relTol 0.001)
- }
-
- test("Naive Bayes Bernoulli with weighted samples") {
- val nPoints = 10000
- val piArray = Array(0.5, 0.3, 0.2).map(math.log)
- val thetaArray = Array(
- Array(0.50, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.40), // label 0
- Array(0.02, 0.70, 0.10, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02), // label 1
- Array(0.02, 0.02, 0.60, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.30) // label 2
- ).map(_.map(math.log))
-
- val testData = generateNaiveBayesInput(piArray, thetaArray, nPoints,
42, "bernoulli").toDF()
- val (overSampledData, weightedData) =
- MLTestingUtils.genEquivalentOversampledAndWeightedInstances(testData,
- "label", "features", 42L)
- val nb = new NaiveBayes().setModelType("bernoulli")
- val unweightedModel = nb.fit(weightedData)
- val overSampledModel = nb.fit(overSampledData)
- val weightedModel = nb.setWeightCol("weight").fit(weightedData)
- assert(weightedModel.theta ~== overSampledModel.theta relTol 0.001)
- assert(weightedModel.pi ~== overSampledModel.pi relTol 0.001)
- assert(unweightedModel.theta !~= overSampledModel.theta relTol 0.001)
- assert(unweightedModel.pi !~= overSampledModel.pi relTol 0.001)
+ test("Naive Bayes with weighted samples") {
+ val numClasses = 3
+ def modelEquals(m1: NaiveBayesModel, m2: NaiveBayesModel): Unit = {
+ assert(m1.pi ~== m2.pi relTol 0.01)
+ assert(m1.theta ~== m2.theta relTol 0.01)
+ }
+ val testParams = Seq(
+ ("bernoulli", bernoulliDataset),
+ ("multinomial", dataset)
+ )
+ testParams.foreach { case (family, dataset) =>
+ // NaiveBayes is sensitive to constant scaling of the weights unless
smoothing is set to 0
+ val estimator = new
NaiveBayes().setSmoothing(0.0).setModelType(family)
--- End diff --
I think it's not practical to set smoothing as 0.0, so it's better to test
NB with none zero smoothing value. If it does not applicable to
```testArbitrarilyScaledWeights```, we can omit it. Or generating two
estimators whose smoothing values are respectively 0.0 and 1.0 for different
test functions.
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